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42
The V*Diagram: A QueryDependent Approach to Moving KNN Queries
, 2008
"... The moving k nearest neighbor (MkNN) query finds the k nearest neighbors of a moving query point continuously. The high potential of reducing the query processing cost as well as the large spectrum of associated applications have attracted considerable attention to this query type from the database ..."
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Cited by 19 (4 self)
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The moving k nearest neighbor (MkNN) query finds the k nearest neighbors of a moving query point continuously. The high potential of reducing the query processing cost as well as the large spectrum of associated applications have attracted considerable attention to this query type from the database community. This paper presents an incremental saferegionbased technique for answering MkNN queries, called the V*Diagram. In general, a safe region is a set of points where the query point can move without changing the query answer. Traditional saferegion approaches compute a safe region based on the data objects but independent of the query location. Our approach exploits the current knowledge of the query point and the search space in addition to the data objects. As a result, the V*Diagram has much smaller IO and computation costs than existing methods. The experimental results show that the V*Diagram outperforms the best existing technique by two orders of magnitude.
Path Oracles for Spatial Networks
, 2009
"... The advent of locationbased services has led to an increased demand for performing operations on spatial networks in real time. The challenge lies in being able to cast operations on spatial networks in terms of relational operators so that they can be performed in the context of a database. A line ..."
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Cited by 11 (5 self)
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The advent of locationbased services has led to an increased demand for performing operations on spatial networks in real time. The challenge lies in being able to cast operations on spatial networks in terms of relational operators so that they can be performed in the context of a database. A linearsized construct termed a path oracle is introduced that compactly encodes the n2 shortest paths between every pair of vertices in a spatial network having n vertices thereby reducing each of the paths to a single tuple in a relational database and enables finding shortest paths by repeated application of a single SQL SELECT operator. The construction of the path oracle is based on the observed coherence between the spatial positions of both source and destination vertices and the shortest paths between them which facilitates the aggregation of source and destination vertices into groups that share common vertices or edges on the shortest paths between them. With the aid of the WellSeparated Pair (WSP) technique, which has been applied to spatial networks using the network distance measure, a path oracle is proposed that takes O(sdn) space, where s is empirically estimated to be around 12 for road networks, but that can retrieve an intermediate link in a shortest path in O(logn) time using a Btree. An additional construct termed the pathdistance oracle of size O(n · max(sd, 1 d ε)) (empirically (n · max(122, 2.5 2 ε))) is proposed that can retrieve an intermediate vertex as well as an εapproximation of the network distances in O(logn) time using a Btree. Experimental results indicate that the proposed oracles are linear in n which means that they are scalable and can enable complicated query processing scenarios on massive spatial network datasets.
Distance Oracles for Spatial Networks
"... Abstract — The popularity of locationbased services and the need to do realtime processing on them has led to an interest in performing queries on transportation networks, such as finding shortest paths and finding nearest neighbors. The challenge is that these operations involve the computation o ..."
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Cited by 10 (4 self)
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Abstract — The popularity of locationbased services and the need to do realtime processing on them has led to an interest in performing queries on transportation networks, such as finding shortest paths and finding nearest neighbors. The challenge is that these operations involve the computation of distance along a spatial network rather than “as the crow flies. ” In many applications an estimate of the distance is sufficient, which can be achieved by use of an oracle. An approximate distance oracle is proposed for spatial networks that exploits the coherence between the spatial position of vertices and the network distance between them. Using this observation, a distance oracle is introduced that is able to obtain the εapproximate network distance between two vertices of the spatial network. The network distance between every pair of vertices in the spatial network is efficiently represented by adapting the wellseparated pair technique to spatial networks. Initially, use is made of an εapproximate distance oracle of size O ( n εd) that is capable of retrieving the approximate network distance in O(logn) time using a Btree. The retrieval time can be theoretically reduced to O(1) time by proposing another εapproximate distance oracle of size O ( nlogn εd) that uses a hash table. Experimental results indicate that the proposed technique is scalable and can be applied to sufficiently large road networks. A 10%approximate oracle (ε = 0.1) on a large network yielded an average error of 0.9 % with 90 % of the answers making an error of 2 % or less and an average retrieval time of 68µ seconds. Finally, a strategy for the integration of the distance oracle into any relational database system as well as using it to perform a variety of spatial queries such as region search, knearest neighbor search, and spatial joins on spatial networks is discussed. I.
Monitoring path nearest neighbor in road networks
 In SIGMOD
, 2009
"... This paper addresses the problem of monitoring the k nearest neighbors to a dynamically changing path in road networks. Given a destination where a user is going to, this new query returns the kNN with respect to the shortest path connecting the destination and the user’s current location, and thus ..."
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Cited by 10 (1 self)
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This paper addresses the problem of monitoring the k nearest neighbors to a dynamically changing path in road networks. Given a destination where a user is going to, this new query returns the kNN with respect to the shortest path connecting the destination and the user’s current location, and thus provides a list of nearest candidates for reference by considering the whole coming journey. We name this query the kPath Nearest Neighbor query (kPNN). As the user is moving and may not always follow the shortest path, the query path keeps changing. The challenge of monitoring the kPNN for an arbitrarily moving user is to dynamically determine the update locations and then refresh the kPNN efficiently. We propose a threephase Bestfirst Network Expansion (BNE) algorithm for monitoring the kPNN and the corresponding shortest path. In the searching phase, the BNE finds the shortest path to the destination, during which a candidate set that guarantees to include the kPNN is generated at the same time. Then in the verification phase, a heuristic algorithm runs for examining candidates’ exact distances to the query path, and it achieves significant reduction in the number of visited nodes. The monitoring phase deals with computing update locations as well as refreshing the kPNN in different user movements. Since determining the network distance is a costly process, an expansion tree and the candidate set are carefully maintained by the BNE algorithm, which can provide efficient update on the shortest path and the kPNN results. Finally, we conduct extensive experiments on real road networks and show that our methods achieve satisfactory performance.
Efficiently Indexing Shortest Paths by Exploiting Symmetry in Graphs
 In EDBT 2009
"... Shortest path queries (SPQ) are essential in many graph analysis and mining tasks. However, answering shortest path queries onthefly on large graphs is costly. To online answer shortest path queries, we may materialize and index shortest paths. However, a straightforward index of all shortest path ..."
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Cited by 9 (2 self)
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Shortest path queries (SPQ) are essential in many graph analysis and mining tasks. However, answering shortest path queries onthefly on large graphs is costly. To online answer shortest path queries, we may materialize and index shortest paths. However, a straightforward index of all shortest paths in a graph of N vertices takes O(N 2) space. In this paper, we tackle the problem of indexing shortest paths and online answering shortest path queries. As many large real graphs are shown richly symmetric, the central idea of our approach is to use graph symmetry to reduce the index size while retaining the correctness and the efficiency of shortest path query answering. Technically, we develop a framework to index a large graph at the orbit level instead of the vertex level so that the number of breadthfirst
Efficient continuous nearest neighbor query in spatial networks using euclidean restriction
 In SSTD
, 2009
"... Abstract. In this paper, we propose an efficient method to answer continuous k nearest neighbor (CkNN) queries in spatial networks. Assuming a moving query object and a set of data objects that make frequent and arbitrary moves on a spatial network with dynamically changing edge weights, CkNN contin ..."
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Cited by 7 (3 self)
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Abstract. In this paper, we propose an efficient method to answer continuous k nearest neighbor (CkNN) queries in spatial networks. Assuming a moving query object and a set of data objects that make frequent and arbitrary moves on a spatial network with dynamically changing edge weights, CkNN continuously monitors the nearest (in network distance) neighboring objects to the query. Previous CkNN methods are inefficient and, hence, fail to scale in large networks with numerous data objects because: 1) they heavily rely on Dijkstrabased blind expansion for network distance computation that incurs excessively redundant cost particularly in large networks, and 2) they blindly map all object location updates to the network disregarding whether the updates are relevant to the CkNN query result. With our method, termed ERCkNN (short for Euclidian Restriction based CkNN), we utilize ER to address both of these shortcomings. Specifically, with ER we enable 1) guided search (rather than blind expansion) for efficient network distance calculation, and 2) localized mapping (rather than blind mapping) to avoid the intolerable cost of redundant object location mapping. We demonstrate the efficiency of ERCkNN via extensive experimental evaluations with real world datasets consisting of a variety of large spatial networks with numerous moving objects. 1
Heuristic algorithms for routesearch queries over geographical data
 GIS
"... In a geographical route search, given search terms, the goal is to find an effective route that (1) starts at a given location, (2) ends at a given location, and (3) travels via geographical entities that are relevant to the given terms. A route is effective if it does not exceed a given distance li ..."
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Cited by 7 (5 self)
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In a geographical route search, given search terms, the goal is to find an effective route that (1) starts at a given location, (2) ends at a given location, and (3) travels via geographical entities that are relevant to the given terms. A route is effective if it does not exceed a given distance limit whereas the ranking scores of the visited entities, with respect to the search terms, are maximal. This paper introduces routesearch queries, suggests three semantics for such queries and deals with the problem of efficiently answering queries under the different semantics. Since the problem of answering routesearch queries is a generalization of the traveling salesman problem, it is unlikely to have an efficient solution, i.e., there is no polynomialtime algorithm that solves the problem (unless P=NP). Hence, in this work we consider heuristics for the problem. Methods for effectively computing routes are presented. The methods are compared analytically and experimentally. For these methods, experiments on both synthetic and realworld data illustrate their efficiency and their effectiveness in computing a route that satisfies the constraints of a routesearch query. Categories and Subject Descriptors
Towards Modeling the Traffic Data on Road Networks
"... A spatiotemporal network is a spatial network (e.g., road network) along with the corresponding timedependent weight (e.g., travel time) for each edge of the network. The design and analysis of policies and plans on spatiotemporal networks (e.g., path planning for locationbased services) require r ..."
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Cited by 5 (4 self)
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A spatiotemporal network is a spatial network (e.g., road network) along with the corresponding timedependent weight (e.g., travel time) for each edge of the network. The design and analysis of policies and plans on spatiotemporal networks (e.g., path planning for locationbased services) require realistic models that accurately represent the temporal behavior of such networks. In this paper, for the first time we propose a traffic modeling framework for road networks that enables 1) generating an accurate temporal model from archived temporal data collected from a spatiotemporal network (so as to be able to publish the temporal model of the spatiotemporal network without having to release the real data), and 2) augmenting any given spatial network model with a corresponding realistic temporal model custombuilt for that specific spatial network (in order to be able to generate a spatiotemporal network model from a solely spatial network model). We validate the accuracy of our proposed modeling framework via experiments. We also used the proposed framework to generate the temporal model of the Los Angeles County freeway network and publish it for public use. 1.
Shortest Path Computation with No Information Leakage
"... Shortest path computation is one of the most common queries in locationbased services (LBSs). Although particularly useful, such queries raise serious privacy concerns. Exposing to a (potentially untrusted) LBS the client’s position and her destination may reveal personal information, such as socia ..."
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Cited by 3 (2 self)
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Shortest path computation is one of the most common queries in locationbased services (LBSs). Although particularly useful, such queries raise serious privacy concerns. Exposing to a (potentially untrusted) LBS the client’s position and her destination may reveal personal information, such as social habits, health condition, shopping preferences, lifestyle choices, etc. The only existing method for privacypreserving shortest path computation follows the obfuscation paradigm; it prevents the LBS from inferring the source and destination of the query with a probability higher than a threshold. This implies, however, that the LBS still deduces some information (albeit not exact) about the client’s location and her destination. In this paper we aim at strong privacy, where the adversary learns nothing about the shortest path query. We achieve this via established private information retrieval techniques, which we treat as blackbox building blocks. Experiments on real, largescale road networks assess the practicality of our schemes. 1.